Publications by authors named "Chiara Cabrelle"
Article Synopsis
- Transfer RNAs (tRNAs) are essential non-coding RNAs involved in protein synthesis but also produce smaller fragments that have various regulatory roles, particularly in cancer.
- These fragments can be categorized into tRNA halves and tRNA-derived fragments, which have specific classifications based on their structure.
- The review covers advancements in sequencing methods for studying tRNA fragments, the development of bioinformatics tools for tRNA research, and the potential of tRNA fragments as biomarkers for cancer diagnostics and prognosis.
Article Synopsis
- * Scientists used a special technique to analyze over 113,000 brain cells and found changes in the way genes work, affecting brain health and inflammation.
- * The mice that drank alcohol showed gene patterns similar to older mice with severe Alzheimer's, suggesting that alcohol might make Alzheimer's worse.
Article Synopsis
- * The tool 'SURFACER' analyzes transcriptomic data to determine the activity of cell surface proteins in breast cancer, helping to identify proteins that significantly affect cellular functions.
- * SURFACER can uncover important insights from various cancer datasets, allowing for the classification of cancers based on surface protein activity, which may lead to new targeted therapies and diagnostic methods.
The COVID-19 pandemic poses a huge problem of public health that requires the implementation of all available means to contrast it, and drugs are one of them. In this context, we observed an unmet need of depicting the continuously evolving scenario of the ongoing drug clinical trials through an easy-to-use, freely accessible online tool. Starting from this consideration, we developed COVIDrugNet ( http://compmedchem.
View Article and Find Full Text PDFNetwork theory provides one of the most potent analysis tools for the study of complex systems. In this paper, we illustrate the network-based perspective in drug research and how it is coherent with the new paradigm of drug discovery. We first present data sources from which networks are built, then show some examples of how the networks can be used to investigate drug-related systems.
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